Pharmaceuticals – Qsutra

What role do t-tests play in Pharmaceutical Processes? When can we apply it?

Umesh — Mon, 05 Oct 2020 13:29:04 +0000

First of all, I would like to set the concept of Hypothesis Testing then we will move step by step to the agenda of this post. Hypothesis testing, what is it? It is a method of drawing inference or conclusion about the sample based on a population as a whole. In simpler words, the process of collecting samples from a population and to determine whether the results are statistically significant or not.

You might be wondering “Why we use t-test specifically and why not other tests?” Because in most of the process scenarios, we do not know the population parameters like mean or standard deviation and hence the only option is t-test.

Suppose in a Pharmaceutical Company, they manufacture lakhs of tablets per day – in such case, it is difficult to know the population parameters like mean and standard deviation. So we apply sample t-tests.

Types of t-test

There are various types of t-test namely one sample t-test, two sample t-test and paired sample t-tests.

We will discuss “When can we apply one sample t-test, two sample t-test and paired sample t-tests?”

We use one sample t-test when we have one sample group. By doing so, we would be able to find whether the mean is significantly different or not with regards to our target specification.

For e.g. in a Pharmaceutical Company, QA officer collects samples of 12 tablets to measure the dissolution rate of a drug. Here, we can find whether the mean of a dissolution rate is significant to our target dissolution rate or not (target = 365 C).

Ho=Target mean value, x bar=Sample mean value

Since our p-value is 0.019 which is less than our alpha value of 0.05, we can conclude that the mean of the dissolution rate is significantly different than the target value.

We use two sample t-test when we have two independent sample groups. By doing so, we would be able to find whether the means are significantly different or not. And we would be able to compare which one is good.

For e.g. in a Pharmaceutical Company, QA officer collects a two sample groups (A and B) each of 12 tablets to measure the dissolution rate of a drug. We can compare which of these types have a better dissolution rate and hence we able to choose the best one.

⊕ = Mean value

Since our p-value is 0.3 which is greater than our alpha value of 0.05, we can conclude that the mean of the dissolution rate is not significantly different. But there is a difference in the variance part (individual value plot).

We use paired sample t-test when we have two dependent sample data of the same subject i.e. to measure the effect on a particular group before and after the treatment (say).

For e.g. during a Clinical Trial, we choose 20 patients and measure their weight before the drug treatment. And again, after the two months, we again measure their weight. By doing so, we can check whether the drug is effective or not. Here we will have two sets of data –

Before treatment

After treatment

It is also called a dependent sample t-test, why it is called so? With reference to the above example, let’s make it clear that the data are collected from the same observations (same patients). Here the patient’s weight is dependent on drug treatment.

As we can see, there are two data groups – Before treatment and After treatment.

Since our p-value is 0.007 which is less than alpha value of 0.05, we can conclude there is a significant difference between the treatments.

We can visualise the differences whether the drug is effective or not based on their weights.

NB – Want to learn the concepts of t-tests and Application in Industries? Attend our Statistical Training Program starting from basic to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Apart from Statistical training, we also conduct Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.

Why Biostatistics is Important in Healthcare and Pharmaceutical Industry?

Umesh — Thu, 06 Aug 2020 04:42:29 +0000

Have you ever come across the term “Biostatistics” before? I guess maximum will tell yes and minimum will tell no or vice versa. Relax, I will leave it to you.

Biostatistics is a branch of Statistics which deals with the data related to Medical Science, Public Health, Medical Research, Epidemiology and so on. In simpler words, when we apply Statistics in Biological Science – it is called Biostatistics.

Let’s discuss few scenarios like

A local PHC wants to know the proportion of people affected by an outbreak of certain disease and to find out the causes behind it and to prevent further circumstances.
During a clinical trial, there are many phases for drug or device development. And now the question is “How will they know whether the trial is going good or bad?”
In a pharmaceutical company, they want to know the dissolution rate of a particular tablet and to know whether it satisfies the desired rate or not. Similarly, they want to know the mean weight of a tablet to compare with other batches. Now, our question is “How will they do the analysis?”

In the above-mentioned scenarios, we apply Biostatistics concepts and do the analysis. Based on the analysis results, we draw inference about the given problem. So basically, Biostatistics is a method that comprises of a collection of data, summarizing, analyzing and drawing inferences.

By now, we have an overall outlook of Biostatistics Application in the Healthcare and Pharma Sector.

We will discuss few basic concepts of Biostatistics.

Data are the first thing which pops out on the head when we talk about any kind of analysis. Since we are dealing with data for analysis, we should know the basic concepts of it. Data are the actual pieces of information that we collect through our process or experiment. It can be categorized into categorical and numerical data. Categorical data are defined in categories or groups. For e.g. Male or Female, Yes or No, etc. Numerical data are further classified into Discrete and Continuous data. Discrete data are the data which take certain values. For e.g. No of patients, Devices, etc. Continuous data are the data which can take any values either fractional or decimal. For e.g. Blood pressure level, Height and Weight of the patient, etc.

Sampling is a method used in Statistical Analysis of a data where a specific number of samples are taken from a population for a study. For e.g. Health Ministry wants to know the opinion or suggestions of recently implemented health programme in Karnataka. Here the population – Karnataka and sample – Bangalore, Mysore, Mangalore, Hubli, etc.

Hypothesis testing is a method to determine whether the results are statistically significant or not. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole is known as Hypothesis Testing.

Measurement System Analysis helps us to detect the amount of variation exists within a measurement system. Suppose during the initial clinical trial phase of a particular medicine, “If the data are not taken properly?” And move forward for further analysis. Then “What would be consequences of it?” Obviously, it may fail later on final phases.

Now, I would like to mention the final overview of Biostatistics Applications in Healthcare and Pharma Sector. It helps to a great extent during the clinical trials by designing the trial, sampling techniques, measurement system analysis and analyzing for appropriate results. We can improve our health policies, new drug policy, assessment of environmental protection guidelines, quality improvement in Hospitals and Pharmaceuticals, and so forth.

NB – Attend our Training Program, to know more about Statistics and Statistical Software. Our Training programs are divided into two sections – Statistical Training and Minitab Software Training. Statistical training starts with basic level to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Now coming to Minitab software training, starts with basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.

We also provide a wide range of Business Analytics Solutions and Business Consulting Services for Organisations to make data-driven decisions and thus enhance their decision support systems.

Is your Sample Size enough for doing the Analysis?

Umesh — Mon, 06 Jul 2020 05:39:46 +0000

I guess this is an important question we should ask ourselves before doing any kind of analysis. We should know the sample size required to do the analysis. Say, if we collect more samples than required – what will happen? We will waste our resources and time. Thus enhanced cost and complexity. On the other hand, if we collect fewer samples than our test may fail and hence less accuracy on results. On an extreme note, our test may fail to detect the important effect and could not meet the test objective. Hence we should determine the optimum sample size required for analysis.

Let’s discuss a very basic example to understand “why the determination of sample size is important in data analysis?” Generally, salt is a very important factor to determine the taste of food. We all know how much work is needed during the cooking process, right! Now, the hard work will be paid off if only we get a significant result i.e. food must be tasty. Say, if we put more salt than required or less salt than required – what will happen? We all know the consequence, right!

To avoid such circumstances in data analysis, “Concept of Power and sample size” comes into play.

What is a Power and Sample size?

It is a concept of estimating adequate sample size required for data analysis to achieve significant results. We already know sample size – it is a count of individuals or samples required for analysis. But you might be thinking about the term “power”. Power analysis tells us a number of sample size required to avoid any type of error in decision making. In other words, we can say that it is a probability of making the right decision irrespective of errors.

I would like to mention a few factors which affect the power analysis are sample size, variability and alpha value (level of significance). We should choose adequate samples so it will not hamper the power analysis. There could be a variation (spreadness of a data) in a process and thus results in a random error. We should use proper sampling procedure and measurement analysis to minimize variation in a process. Alpha value represents the probability of making the wrong decision when it is actually true. The common alpha values are 1% (0.01) and 5% (0.05).

In a ball bearing manufacturing industry, engineers are concerned that the bearing diameter has shifted away from the target of 0.6 cm. They consider a difference of 0.01 cm to warrant adjusting the equipment. From the historical data, the standard deviation is 0.005 cm.

Now the question is “How much data is needed for analysis?”

With reference from the power curve – for doing the analysis we need a sample size of 5 where alpha value is 0.05 and power is 0.9.

What are the common power values used?

When determining the sample size, we should put the optimum power value as to avoid any kind of error. Suppose in a test, if it has low power value than it might fail to detect an effect and if it has a higher power value than small effects might seem to be significant. Some of the common power values used are 0.8 and 0.9. Let’s say if your study has 90% power than it has a 90% chance of detecting an effect that exists.

There are various ways to determine power and sample size. The easiest and simplest way to determine it is by using the “Power and Sample size” option in MINITAB. With Recent Release, Minitab now Available on both Desktop and the Cloud.

NB – Want to learn, How to run Power & Sample Size Analysis in Minitab Software? Attend our Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. Apart from Minitab training, we also conduct basic and advanced Statistical training. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc.

Also we offer solutions to build a robust Enterprise Decisions Management system that can be very effective for organisational Decision Making.

Is your Data Reliable enough to do the Analysis?

Umesh — Mon, 09 Mar 2020 05:23:03 +0000

Data – What we need to do the analysis? But how much we are sure that the data we have collected for analysis is reliable enough! The main agenda of this post is to discuss data reliability, it’s importance and how to achieve it.

Let’s start with a brief example, suppose during a Clinical Trial “If the data are not taken properly?” And proceed further for analysis. Then “What will be the consequences of it?” Clearly, it may fail later and results in loss of lives, resources and time. Thus, the device measuring the host condition should produce reliable results.

Now, I would like to introduce the term called “Measurement System Analysis (MSA)” which helps to determine the reliability of a data for analysis. You must be curious to know about it, right!

Generally, MSA is a method to determine the measurement system in a process. The measurement system is a collection of measurement devices, measurement procedures and operators that are used to obtain a measurement. MSA helps us to detect the variation (i.e difference or disparity) exists in a measurement system. So we should evaluate our measurement system before conducting statistical process control, design of experiment, Predictive Analytics, or other statistical analysis. By doing so, we can be sure that our data are reliable for analysis.

We can apply MSA for two types of data namely attribute and continuous data.

Attribute data - When data collected are in the form of count or categorized in the form of group. For e.g. accept or reject, good or bad, and so on. In such scenario, we use Attribute agreement analysis.
Continuous data - When data collected are in the form of fractional or decimal value. For e.g. weight, length, time, temperature, etc. In such scenario, we use a Gage R & R analysis.

MSA is classified into Attribute agreement analysis and Gage R&R analysis based on data types used.

Example

Suppose in a Wheel Manufacturing Company, operators use gage to measure the diameter of the wheel. Are we 100% sure that the collected data is measured correctly or not?

With reference from the Gage Run Chart – Three operators measure 10 wheels, three times per wheel randomly. Here, we can examine the differences in measurements between operators. We can see that Operator B does not measure consistently, and Operator C usually measures lower than the other operators.

Let’s discuss a few terminologies used in measurement system analysis.

Accuracy – It is the difference between the measurement of a part and its actual value.
Bias – It is the measurement difference between the observed value and true value.
Linearity – It is the difference or a change in bias value w.r.t its size.
Stability – It is a measurement of how well the system performs over time or we can say, it is a change in bias value over time.
Precision – It is the closeness between the measured value and the true value.
Repeatability – It is a variation which occurs when the same operator measures the same part with the same device.
Reproducibility – It is a variation which occurs when the different operators measure the same part with the same device.

Why MSA is important?

It is a prerequisite for successfully implementing Lean Manufacturing, Six Sigma and Lean Six Sigma projects. It is also useful in the implementation of SPC, DOE, Enterprise Prediction Management, Enterprise Risk Management, Enterprise Risk Management, and Enterprise Quality Management processes. It gives the better reliability of the measurement system in a statistical way.

NB – Want to learn, How to run Measurement System Analysis in Minitab Software? Attend our Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. Apart from Minitab training, we also conduct basic and advanced Statistical training. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc.

We provide a wide range of Analytics & AI Solutions like Business Analytics, Digital Process Automation, Enterprise Information Management, Enterprise Decisions Management and Business Consulting Services for Organisations to enhance their decision support systems.

What are the Applications of ANOVA in the field of Healthcare and Pharmaceutical Industry?

Umesh — Sun, 08 Mar 2020 06:08:29 +0000

Healthcare Industry is the largest sector in India which comprises of hospitals, pharmaceuticals, diagnostics, telemedicine, medical tourism and so on. We will discuss a few applications of ANOVA in the field of pharmaceutical and other health services. Before going to ANOVA, let us create some basic background and will be helpful to grasp in a wider way.

First of all, I would like to introduce the term called “Sampling”. Sampling plays an important role in healthcare data analysis, in fact in any kind of data analysis. It is a process of selecting a sample from a population for a specify study or analysis. For example, we want to conduct a study on a diabetes patient and here it is not possible to conduct a study on the whole population (say India) due to certain factors. So, what will we do? We will take a sample (say Karnataka, West Bengal, Rajasthan, Himachal Pradesh, Meghalaya) and do the study. By now, we must be clear about the concept of the sampling process.

Next, we will dig little on “Hypothesis testing” – based on a collected sample we need to draw inferences or to make a conclusion about the data, we need hypothesis testing. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole is known as hypothesis testing.

Now, let’s discuss the basic sample tests viz. one sample t-test and two-sample t-test.

Suppose in a Pharmaceutical Company, QA officer collects a two sample groups (A and B) each of 10 tablets to measure the dissolution rate of a drug. We can compare which of these types have a better dissolution rate and hence we able to choose the best one. Here, we apply a two-sample t-test.

By now we know the basic concepts of sample tests, when we have one sample, we use one sample t-test and when we have two sample groups, we use two-sample t-test. Isn’t it simple?

In many real-life scenarios, we will have more than two sample groups say three or four sample groups then “What can we do?” In this case, ANOVA comes into play. Finally……right

ANOVA (known as Analysis of Variance) is a technique which is used to check whether the means of two or more sample groups are statistically different or not.

Suppose in the Healthcare Industry, we can use the ANOVA test to compare different medications and the effect on patients. If a company has 3 different medications for treating ulcers and by using ANOVA, we can determine the effectiveness of treating them. We can compare which medication works better for treatment and hence we able to choose the best one.

During the vendor quality evaluation, we can use ANOVA to evaluate the quality of product supplies received from different vendors. Similarly, we can apply in various healthcare scenarios.

Suppose in a Pharmaceutical Company, QA officer collects four sample groups (A, B, C & D) each of 6 tablets to measure the hardness of a tablet.

Since our p-value is 0.004 (From ANOVA Table) which is less than our alpha value 0.05, we can conclude that the means are significantly different from each other. Similarly, on an interval plot, we can see the mean differences between each type.

Let’s discuss a few of the assumption it holds.

NB – Want to learn the concepts of ANOVA and Application in Industries? Attend our Statistical Training Program starting from basic to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Apart from Statistical training, we also conduct Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.